Extending Velocity Sensor Bandwidth by Compensating Temperature Dependency Based on BP Neural Network

Zhao, Bo; Shi, Weijia; Sun, Ding; Li, Jiaxin; Feng, Li; Tan, Jiubin

doi:10.1109/access.2019.2948261

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Various neural network-based architectures have been developed for numerous applications based on the original model. One of the famous neural networks is BPNN, a machine learning method that has been used for years for many applications [27], [28], [29], and shows a very good performance [30], [31], [32], [33], [34], [35], [36], effective [37], and accurate [38].…”

Section: B Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Sumari¹,

Adinandra²,

Syulistyo³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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A hostile or unfriendly aircraft will mostly fly at low-level altitude or hide behind natural obstacles to avoid Radar detection. One of the ways to detect and recognize while at the same time identifying such aircraft is to perform air observation from the ground. A technique called Visual Aircraft Recognition (VACR) has been practiced in training soldiers to recognize and find an incoming aircraft from a distance using binoculars. Remembering so many types of aircraft have their challenge. To ease the task, we have designed and developed an intelligent military aircraft recognition and identification system using the combination of Back Propagation Neural Networks (BPNN) and Information Fusion to speed up the recognition and identification. We use 13 aircraft features fused into five primary ones as the inputs to the BPNN for the recognition, while the identification uses Hamming Distance to the recognition results. With 155 data consisting of 85 military aircraft and helicopters and 70 civilian aircraft and helicopters and applying the 80:20 scheme for the training and test data, our system can obtain 95.33% and 87% accuracy at the training phase and the test phase. It also succeeds in recognizing and identifying a new military aircraft that is not in the dataset, while the Information Fusion can speed up the recognition and identification by up to 6 seconds. This impacts the acceleration of aircraft recognition and identification.

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Section: B Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Sumari¹,

Adinandra²,

Syulistyo³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Software compensation is more cost-effective, flexible, and accurate than hardware compensation, making it more suitable for practical applications. Numerous software compensation methods have been proposed by researchers, such as least squares method (LSM), wavelet transform (WT) [ 10 ], and various artificial neural networks (ANNs) [ 11 , 12 , 13 , 14 ]. The LSM is more suitable for linear problems, and the error caused by temperature is usually non-linear.…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

Dong,

Xu,

Cao

et al. 2023

Sensors

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The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

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Non-dispersive infrared SF6 sensor with temperature compensation using ISSA_BP neural network

Liang,

Wang,

et al. 2024

Sensors and Actuators A: Physical

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Extending Velocity Sensor Bandwidth by Compensating Temperature Dependency Based on BP Neural Network

Cited by 5 publications

References 27 publications

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

Non-dispersive infrared SF6 sensor with temperature compensation using ISSA_BP neural network

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